Lung computed tomography (CT) technology has been widely used by physicians in the screening and diagnosis of lung cancer. From lung CT images, the physician can search for nodules and make judgments on their malignancy based on the statistics of the nodules, including shape, size, etc. A very important piece of information is the status change of the nodules over time, such as changes in shape, size, and density. One of the most significant quantitative measurements is the growth rate of lung nodules during a period of time. It is therefore crucial to identify the correspondence of the same nodule in two or more lung CT image sets captured at different time frames.
So far, this task has been done manually, and therefore it is tedious, slow, and error prone because of the tremendous amount of data. Because the CT data are 3D images, the task becomes very difficult for the physician, if at all achievable. In current clinical practice, the physician is required to scan through 2D slices of the 3D image data one by one and try to find the correspondence of a nodule in two image sets. The number of slices for a single data set is as large as several hundreds, and a single slice contains 250,000 pixels. Moreover, the imaging condition for the patient in the CT studies may be varied and the organ and the body may be deformed between two such studies. In many cases, it is hard to tell if a nodule has disappeared after a period of time or still exists because the physician is not able to identify the correspondence between images.
Fast registration of local volumes-of-interest (VOI) from large 3D image data is very often needed in medical image analysis systems, such as the systems for analyzing lung CT images. For example, in the screening and diagnosis of lung cancer, very important pieces of information are the presence of a new nodule, the absence of a previously presented nodule, and the growth rate of a lung nodule. It is therefore crucial to identify the correspondence of the same nodule in two or more lung CT image sets captured at different time frames. In most cases, the properties of the nodule and its surrounding structures are locally distinct, and therefore the registration of local VOI's is sufficient for identifying the correspondence of nodules.
What is needed is a graphical user interface (GUI) that provides convenient examination of two or more image sets allowing the user to immediately identify an object-of-interest on one image set and thereby automatically identifies a corresponding region of interest on the remaining image set by virtue of an automated system that avoids full volume registration, but performs fast and accurate registration of two local VOI's. The GUI should also provide a various set of functions to facilitate examination and comparison, such as synchronized scrolling of the slices in the two data sets.